Analyzing visual imagery for emergency drone landing on unknown environments

Abstract

Autonomous landing is a fundamental aspect of drone operations which is being focused upon by the industry, with ever-increasing demands on safety. As the drones are likely to become indispensable vehicles in near future, they are expected to succeed in automatically recognizing a landing spot from the nearby points, maneuvering toward it, and ultimately, performing a safe landing. Accordingly, this paper investigates the idea of vision-based location detection on the ground for an automated emergency response system which can continuously monitor the environment and spot safe places when needed. A convolutional neural network which learns from image-based feature representation at multiple scales is introduced. The model takes the ground images, assign significance to various aspects in them and recognize the landing spots. The results provided support for the model, with accurate classification of ground image according to their visual content. They also demonstrate the feasibility of computationally inexpensive implementation of the model on a small computer that can be easily embedded on a drone.

Onboard Processing of Drone Imagery for Military Vehicles Classification Using Enhanced YOLOv5

“…According to Ref. [8], a CNN is introduced that learns from image-based feature representation at various sizes. The model takes the ground photos, gives different parts of their significance, and determines the landing sites.…”

Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events

“…Very early on, the capabilities of image processing arose to help the pilot in flying and maintaining safety. Image processing proposals have been published for landing area recognition [5], as support to an emergency landing operation [6], and as a detect-and-avoid on-board system [7].…”

Landing site selection for UAV in unknown environment using surface inclination

“…By taking advantage of vision sensors and vision-based approaches, a pre-trained model is used to first segment the area to identify obstacle free areas and classify the slope. This information is further fused using linguistic rules and find the candidate sites in the map [21]. Inclination is a vital factor for the safe landing of UAV, and it is very less explored due to the pretext of using elevation and terrain models.…”